{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#作用一：可以展示数据的分布和聚合情况\n",
    "#作用二：得到趋势线公式\n",
    "#作用三：辅助制图\n",
    "\n",
    "from matplotlib import pyplot as plt, font_manager\n",
    "\n",
    "# 散点图\n",
    "y = [11, 17, 16, 11, 12, 11, 12, 6, 6, 7, 8, 9, 12, 15, 14, 17, 18, 21, 16, 17, 20, 14, 15, 15, 15, 19, 21, 22, 22, 22,\n",
    "     23]\n",
    "x = range( 1, 32 )\n",
    "\n",
    "# 设置图形大小\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "# 使用scatter绘制散点图\n",
    "plt.scatter( x, y, label='3月份' )\n",
    "# 调整x轴的刻度\n",
    "my_font = font_manager.FontProperties( fname='C:\\Windows\\Fonts\\STSONG.TTF', size=10 )\n",
    "\n",
    "xticks_labels = ['3月{}日'.format(i) for i in x]\n",
    "plt.xticks( x[::3], xticks_labels[::3], fontproperties=my_font,rotation=45)\n",
    "plt.xlabel( ' 日 期 ', fontproperties=my_font )\n",
    "plt.ylabel( '温度', fontproperties=my_font )\n",
    "# 图 例\n",
    "plt.legend( prop=my_font )\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}